27,478 research outputs found
KISS: Stochastic Packet Inspection Classifier for UDP Traffic
This paper proposes KISS, a novel Internet classifica- tion engine. Motivated by the expected raise of UDP traffic, which stems from the momentum of Peer-to-Peer (P2P) streaming appli- cations, we propose a novel classification framework that leverages on statistical characterization of payload. Statistical signatures are derived by the means of a Chi-Square-like test, which extracts the protocol "format," but ignores the protocol "semantic" and "synchronization" rules. The signatures feed a decision process based either on the geometric distance among samples, or on Sup- port Vector Machines. KISS is very accurate, and its signatures are intrinsically robust to packet sampling, reordering, and flow asym- metry, so that it can be used on almost any network. KISS is tested in different scenarios, considering traditional client-server proto- cols, VoIP, and both traditional and new P2P Internet applications. Results are astonishing. The average True Positive percentage is 99.6%, with the worst case equal to 98.1,% while results are al- most perfect when dealing with new P2P streaming applications
Mining Unclassified Traffic Using Automatic Clustering Techniques
In this paper we present a fully unsupervised algorithm to identify classes of traffic inside an aggregate. The algorithm leverages on the K-means clustering algorithm, augmented with a mechanism to automatically determine the number of traffic clusters. The signatures used for clustering are statistical representations of the application layer protocols. The proposed technique is extensively tested considering UDP traffic traces collected from operative networks. Performance tests show that it can clusterize the traffic in few tens of pure clusters, achieving an accuracy above 95%. Results are promising and suggest that the proposed approach might effectively be used for automatic traffic monitoring, e.g., to identify the birth of new applications and protocols, or the presence of anomalous or unexpected traffi
Passive characterization of sopcast usage in residential ISPs
AbstractāIn this paper we present an extensive analysis of traffic generated by SopCast users and collected from operative networks of three national ISPs in Europe. After more than a year of continuous monitoring, we present results about the popularity of SopCast which is the largely preferred application in the studied networks. We focus on analysis of (i) application and bandwidth usage at different time scales, (ii) peer lifetime, arrival and departure processes, (iii) peer localization in the world. Results provide useful insights into users ā behavior, including their attitude towards P2P-TV application usage and the conse-quent generated load on the network, that is quite variable based on the access technology and geographical location. Our findings are interesting to Researchers interested in the investigation of users ā attitude towards P2P-TV services, to foresee new trends in the future usage of the Internet, and to augment the design of their application. I
Characterization of P2P IPTV Traffic: Scaling Analysis
P2P IPTV applications arise on the Internet and will be massively used in the
future. It is expected that P2P IPTV will contribute to increase the overall
Internet traffic. In this context, it is important to measure the impact of P2P
IPTV on the networks and to characterize this traffic. Dur- ing the 2006 FIFA
World Cup, we performed an extensive measurement campaign. We measured network
traffic generated by broadcasting soc- cer games by the most popular P2P IPTV
applications, namely PPLive, PPStream, SOPCast and TVAnts. From the collected
data, we charac- terized the P2P IPTV traffic structure at different time
scales by using wavelet based transform method. To the best of our knowledge,
this is the first work, which presents a complete multiscale analysis of the
P2P IPTV traffic. Our results show that the scaling properties of the TCP
traffic present periodic behavior whereas the UDP traffic is stationary and
lead to long- range depedency characteristics. For all the applications, the
download traffic has different characteristics than the upload traffic. The
signaling traffic has a significant impact on the download traffic but it has
negligible impact on the upload. Both sides of the traffic and its granularity
has to be taken into account to design accurate P2P IPTV traffic models.Comment: 27p, submitted to a conferenc
Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment
Understanding mobile traffic patterns of large scale cellular towers in urban
environment is extremely valuable for Internet service providers, mobile users,
and government managers of modern metropolis. This paper aims at extracting and
modeling the traffic patterns of large scale towers deployed in a metropolitan
city. To achieve this goal, we need to address several challenges, including
lack of appropriate tools for processing large scale traffic measurement data,
unknown traffic patterns, as well as handling complicated factors of urban
ecology and human behaviors that affect traffic patterns. Our core contribution
is a powerful model which combines three dimensional information (time,
locations of towers, and traffic frequency spectrum) to extract and model the
traffic patterns of thousands of cellular towers. Our empirical analysis
reveals the following important observations. First, only five basic
time-domain traffic patterns exist among the 9,600 cellular towers. Second,
each of the extracted traffic pattern maps to one type of geographical
locations related to urban ecology, including residential area, business
district, transport, entertainment, and comprehensive area. Third, our
frequency-domain traffic spectrum analysis suggests that the traffic of any
tower among the 9,600 can be constructed using a linear combination of four
primary components corresponding to human activity behaviors. We believe that
the proposed traffic patterns extraction and modeling methodology, combined
with the empirical analysis on the mobile traffic, pave the way toward a deep
understanding of the traffic patterns of large scale cellular towers in modern
metropolis.Comment: To appear at IMC 201
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